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Grey- and rough-set-based seasonal disaster predictions: an analysis of flood data in India

Author

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  • R. Rajesh

    (ABV - Indian Institute of Information Technology & Management)

  • Chandrasekharan Rajendran

    (Indian Institute of Technology Madras)

Abstract

In a globally competitive market, companies attempt to foresee the occurrences of any catastrophe that may cause disruptions in their supply chains. Indian subcontinent is prone to frequent disasters related to floods and cyclones. It is essential for any supply chain operating in India to predict the occurrence of any such disasters. By doing so, the disaster management and the relief teams can prepare for the worst. This research makes use of a grey seasonal disaster prediction model to forecast the possible occurrence of any flood-related disasters in India. Flood data of major flood occurrences for a period of 10 years (2007–2017) have been taken for analysis in this context. We have established a grey model of the first order and with one variable, GM (1, 1), for prediction; from the results, we observe there are high chances of occurrence of a flood-related disaster in India during the early monsoon period (June–August), in both 2018 and 2020. By observing the prediction sequences on fatalities, there is likelihood that the death toll may rise above 100 and the flood can result in disastrous consequences. Also, the results of prediction are compared using an enhanced rough-set-based prediction model. From the results of rough-set-based prediction model, there are chances of a severe flood in mid-2018 in India. The results will be useful for organizations, NGOs and State Governments to carefully plan their supply and logistics network in the event of disasters. Graphic abstract Proposed methodology of grey seasonal disaster prediction for floods.

Suggested Citation

  • R. Rajesh & Chandrasekharan Rajendran, 2019. "Grey- and rough-set-based seasonal disaster predictions: an analysis of flood data in India," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 97(1), pages 395-435, May.
    • Handle: RePEc:spr:nathaz:v:97:y:2019:i:1:d:10.1007_s11069-019-03651-y
    DOI: 10.1007/s11069-019-03651-y
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    References listed on IDEAS

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    Cited by:

    1. Qingwei Xu & Kaili Xu, 2020. "Statistical Analysis and Prediction of Fatal Accidents in the Metallurgical Industry in China," IJERPH, MDPI, vol. 17(11), pages 1-20, May.
    2. Rajesh, R. & Agariya, Arun Kumar & Rajendran, Chandrasekharan, 2021. "Predicting resilience in retailing using grey theory and moving probability based Markov models," Journal of Retailing and Consumer Services, Elsevier, vol. 62(C).
    3. R. Rajesh, 2023. "Grey Markov Models for Predicting the Social Sustainability Performances of Firms," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 168(1), pages 297-351, August.

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